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Mark Buehner and Ahmed Mahidjiba

ensemble itself, all analyses in the perturbed 4D-Var assimilation cycles are performed using the same background-error covariances that do not depend on the ensembles. They found that the perturbations obtained from the ensemble 4D-Var led to underdispersive and less skillful ensemble forecasts in the northern extratropics than when using SVs. However, relative to the use of SVs alone, an overall improvement in the ensemble forecasts was obtained, especially for the tropics, when the perturbations

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

shown). In the two extratropical regions for which the scores are presented, use of the EnKF ensemble mean analyses resulted in the same relative ranking of the experiments as when using the 4D-Var-Bnmc analyses. Most verification scores are computed separately for the two extratropical regions and the tropics and for which 20° latitude is used to define the transition between tropics and extratropics. The maximum number of radiosonde stations available for verification in the three regions is 446

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

inflation, with which the forecast or first-guess ensemble spread is inflated by a factor slightly greater than unity. In this study, the inflation parameters are defined separately in the NH (20°–90°N) and SH (20°–90°S) to account for the large difference of observing density. The parameters for each hemisphere are linearly interpolated in the tropics (20°S–20°N). Since the upper layers have smaller observing density, the inflation is linearly reduced at the top eight layers so that the top layer has

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Thomas M. Hamill and Jeffrey S. Whitaker

tuning a globally constant covariance inflation to produce spread consistent with errors at all locations. Here, zonal- and time-averaged spread, RMS error, and bias (ensemble-mean forecast minus truth) are plotted for the minimum-error inflation rate/length scale, the dot in Fig. 4 . Spread was generally smaller than error, but was greater than error for tropical temperatures. When spread was further increased, temperature and low-level wind errors increased in the tropics (not shown), indicating

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Takuya Kawabata, Tohru Kuroda, Hiromu Seko, and Kazuo Saito

model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm . J. Atmos. Sci. , 55 , 835 – 852 . Sun , J. , and Y. Zhang , 2008 : Analysis and prediction of a squall line observed during IHOP using multiple WSR-88D observations . Mon. Wea. Rev. , 136 , 2364 – 2388 . Tsuyuki , T. , 1996a : Variational data assimilation in the tropics using precipitation data. Part I: Column model . Meteor. Atmos. Phys. , 60 , 87 – 104 . Tsuyuki , T. , 1996b

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Craig H. Bishop, Daniel Hodyss, Peter Steinle, Holly Sims, Adam M. Clayton, Andrew C. Lorenc, Dale M. Barker, and Mark Buehner

(TLM) and adjoint, outperformed versions of operational 4D-VAR and ensemble Kalman filter (EnKF) data assimilation (DA) schemes. They also showed that an ensemble-4D-VAR scheme that only used localized 4D ensemble covariances and did not use a TLM or adjoint, outperformed a version of the operational 4D-VAR scheme both in the tropics and Southern Hemisphere, but not in the northern extratropics. They also mention that, without including the cost of generating the ensemble, ensemble-4D-Var could be

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José A. Aravéquia, Istvan Szunyogh, Elana J. Fertig, Eugenia Kalnay, David Kuhl, and Eric J. Kostelich

and from 1.35 to 1.25 in the NH extratropics, while ρ changes smoothly throughout the tropics (between 25°S and 25°N) from the values of the SH extratropics to the values of the NH extratropics. The matrix , whose columns are the local weight vectors for the ensemble perturbations, is computed by . The weight vector is added to each row of . The columns of the resulting matrix are the members of the ensemble of weight vectors . c. Satellite radiance observations The assimilation procedure

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Chiara Piccolo

, which includes 24-h model error integration from differences between forecasts of different lengths valid at the same time. In the extratropics the growth of random initial conditions sampled from static matrices is smaller than the growth of the ensemble spread for both temperature and zonal winds, while generally the opposite occurs in the tropics. The lack of error growth from random initial condition perturbations may be explained by the covariance model, defined by a series of transformations

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Craig H. Bishop and Daniel Hodyss

Lorenc (2003) , Buehner (2005) , Buehner et al. (2010a , b) , Wang et al. (2007) , Liu et al. (2009) , and others. Buehner et al. (2010a , b) found that an ensemble 4D-VAR scheme that used NECL but did not use a tangent linear model or adjoint, outperformed a version of the operational 4D-VAR scheme both in the tropics and Southern Hemisphere, but not in the northern extratropics. The promise of improving 4D-VAR with ensemble covariances is a particularly strong motivation for this study

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